人工智能和可穿戴设备用于早期检测认知障碍和痴呆症:系统审查
Ander Cejudo1,2, Markel Arrojo1, Cristina Martín1,2,3
1Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain.
Journal of medical Internet research
|February 23, 2026
概括
可穿戴设备通过分析睡眠和活动模式,显示出早期痴呆症检测的前景. 持续监测提供了可扩展的,个性化的风险降低策略,超越了偶尔的评估.
科学领域:
- 数字健康和可穿戴技术
- 神经科学与认知衰老
- 发现生物标志物的发现.
背景情况:
- 传统的认知查方法是偶发的,可能会错过早期痴呆症指标.
- 可穿戴设备为数字生物标志物生成提供睡眠,活动和昼夜节律的持续监测.
- 关于可穿戴设备对认知健康的现有证据在设备,分析和结果之间分散.
研究的目的:
- 综合和评估最近关于可穿戴设备的证据,以早期检测和预防认知障碍和痴呆症.
- 专注于设备类型,认知结果,分析方法和预防相关性.
- 批判性地评估基于可穿戴设备的认知健康数字表型的方法成熟度.
主要方法:
- 在PubMed,Scopus,ACM数字图书馆和SpringerLink (2020年1月至2025年12月) 的系统文献搜索.
- 包括的研究:平均年龄≥50岁,≥24小时可穿戴数据,验证的认知结果.
- 不包括:审查,协议,仅用于智能手机,药理干预. 使用了叙事合成 (PRISMA 2020).
主要成果:
- 包括49项研究,主要使用研究级的动图 (87.8%).
- 常见的认知结果:迷你精神状态检查 (36.7%),ICD-10诊断 (14.3%),蒙特利尔认知评估 (14.3%).
- 统计分析将睡眠/活动中断与认知能力低下联系在一起;ML/DL显示AUC为0.70-0.95. 26.5% 解决了预测.
结论:
- 由可穿戴产生的行为标志物显示了认知障碍早期风险分层的潜力.
- 该领域正在使用数字表型化在临床前阶段向人工智能驱动的预测迈进.
- 持续监控使微妙变化的可扩展检测为个性化降低风险.
关键词:
动画摄影 (actigraphy) 是一种摄影方式.人工智能的人工智能是人工智能.昼夜节律是指昼夜节律的节奏.认知障碍是一种认知障碍.痴呆症 痴呆症是一种痴呆症.移动健康 移动健康 移动健康 移动健康机器学习是机器学习.移动健康的移动健康预防 预防 预防睡眠 睡眠 睡眠 睡眠 睡眠可穿戴设备可穿戴设备.更多相关视频
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